Automatic tuning and adaptation for PID controllers - a survey

Åström, Karl Johan; Hägglund, Tore; Hang, Chang C.; Ho, Weng Khuen

doi:10.1016/0967-0661(93)91394-c

Cited by 596 publications

(52 citation statements)

References 14 publications

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“…The control gains can be adjusted to account for the level of noise in the feedback signal (i.e., which could result in high frequency stimulation) versus performance metrics and robustness. While various approaches have been documented for systematically adjusting the control gains for PD-like controllers (Åström et al, 1993;Killingsworth and Krstic, 2006), an empirical approach was used to obtain the results in this article. The results were obtained from the experiments performed on able-bodied subjects and any voluntary contributions from them may create a bias in the results.…”

Section: Discussionmentioning

confidence: 99%

A Non-Linear Control Method to Compensate for Muscle Fatigue during Neuromuscular Electrical Stimulation

et al. 2017

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Neuromuscular electrical stimulation (NMES) is a promising technique to artificially activate muscles as a means to potentially restore the capability to perform functional tasks in persons with neurological disorders. A pervasive problem with NMES is that overstimulation of the muscle (among other factors) leads to rapid muscle fatigue, which limits the use of clinical and commercial NMES systems. The objective of this article is to develop an NMES controller that incorporates the effects of muscle fatigue during NMES-induced non-isometric contraction of the human quadriceps femoris muscle. Our previous work that used the RISE class of non-linear controllers cannot accommodate fatigue and muscle activation dynamics. A totally new control design approach and associated stability proof is required to derive a new class of NMES control design that accounts for muscle fatigue dynamics and a first-order activation dynamics, in addition to the second-order musculoskeletal dynamics. Motivated from a control method for robotic systems in a strict-feedback form, a backstepping based-non-linear NMES controller was designed to accommodate for the additional muscle activation dynamics. Further, experimentally identified estimates of the fatigue and activation dynamics were incorporated in the control design. The developed controller uses a neural networkbased estimate of the musculoskeletal dynamics and error due to fatigue estimation. A globally uniformly ultimately bounded stability is proven the new controller that accounts for an uncertain non-linear muscle model and bounded non-linear disturbances (e.g., spasticity and changing load dynamics). The developed controller was validated through experiments on the left and right legs of 3 able-bodied subjects and was compared with a proportional-derivative (PD) controller and a PD augmented with a neural network. The statistical analysis showed improved control performance compared with the PD controller.

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Section: Discussionmentioning

confidence: 99%

A Non-Linear Control Method to Compensate for Muscle Fatigue during Neuromuscular Electrical Stimulation

et al. 2017

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“…As process control performance and network performance are closely coupled, a common evaluation framework for joint system design is essential and still missing. Although there exists a large and rich body of literature on control theory [9] and wireless networking [10], resulting from decades of research and development, a joint performance analysis of a system comprised of both components turns out to be difficult. On the other hand, simulation has proven to be a practical and economic approach to study the behavior of complex systems and evaluate competing solutions before field deployment [11].…”

Section: Introductionmentioning

confidence: 99%

A simulation framework for industrial wireless networks and process control systems

Liu

Candell

Lee

et al. 2016

2016 IEEE World Conference on Factory Communication Systems (WFCS)

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Factory and process automation systems are increasingly employing information and communications technologies to facilitate data sharing and analysis in integrated control operations. Wireless connections provide flexible access to a variety of field instruments and reduce network installation and maintenance costs. This serves as an incentive for the adoption of industrial wireless networks based on standards such as the WirelessHART and ISA100.11a in factory control systems. However, process control systems vary greatly and have diverse wireless networking requirements in different applications. These requirements include deterministic transmissions in the shared wireless bandwidth, low-cost operation, long-term durability, and high reliability in the harsh radio propagation environment.It is an open question whether a generic wireless technology would meet the requirements of industrial process control. In this paper, we propose a novel simulation framework for performance evaluation of wireless networks in factory and process automation systems. We select a typical process control plant model, specifically the Tennessee Eastman Challenge (TE) Model, and define the interfaces between the process simulator and the wireless network simulator. We develop a model of the protocol stack of the WirelessHART specification in the OMNET++ simulation engine as a typical industrial wireless network. We present simulation results that validate the prospect of using WirelessHART in the TE plant, and we evaluate the impact of various wireless network configurations on the plant operation. Given its modular design, the proposed simulation framework can be easily used to evaluate the performance of other industrial wireless networks in conjunction with a variety of process control systems.Index Terms-industrial wireless networks, sensor networks, factory and process automation networks, WirelessHART, network simulation.

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“…Ziegler-Nichols tuning rules [13], were based on Integral Square Error (ISE) minimization, nevertheless it is well-known that this optimization principle leads to rather less damped oscillations of control response. Better results in this respect are obtained with the use of Integral Absolute Error (IAE) minimization and this criterion has been more preferred in the last decades [1]. A survey of various tuning approaches based on IAE minimization is in [7].…”

Section: Introductionmentioning

confidence: 99%

IAE optimization of delayed PID control loops using dimensional analysis approach

Fišer

Zı́tek

Kučera

2014

2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP)

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A specific issue of the PID control loop with time delay is the contradiction of its infinite-order dynamics with the only three controller parameters used to adjusting its behaviour. For selecting the optimum PID parameters the IAE criterion has been used as performance measure of the disturbance rejection in the investigated control loop. In order to obtain the results in a generic form the dimensionless description of the control loop, originally introduced in [14], was applied. The plant model is based on the dimensional analysis, reducing the relevant parameters of the control loop to a pair of similarity numbers, namely the so-called laggardness (ϑ ) and swingability ( λ ) numbers. The IAE optimum search is performed by means of the gradientbased method over a representative set of options of λ , ϑ , and the optimum PID controller parameters are assessed for the whole considered area of λ ,ϑ . To each of the optimum rejection responses a characteristic quasi-polynomial corresponds and then the rightmost part of its spectrum can be evaluated. The large scale spectral analysis has shown that for all of the investigated options a double pair group of poles results as dominant in the control loop dynamics. The final result of the paper consists in summarizing the IAE optimum settings of PID and comparing the obtained natural frequency angles of the control responses and their damping.

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Automatic tuning and adaptation for PID controllers - a survey

Cited by 596 publications

References 14 publications

A Non-Linear Control Method to Compensate for Muscle Fatigue during Neuromuscular Electrical Stimulation

A Non-Linear Control Method to Compensate for Muscle Fatigue during Neuromuscular Electrical Stimulation

A simulation framework for industrial wireless networks and process control systems

IAE optimization of delayed PID control loops using dimensional analysis approach

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